4 Types of Big Data Technologies (+ Management Tools)

As technology companies like Amazon, Meta, and Google continue to grow and integrate with our lives, they leverage big data technologies to monitor sales, improve supply chain efficiency and customer satisfaction, and predict future business outcomes. Currently, there is so much big data that the International Data Corporation (IDC) predicts the “Global Datasphere” will grow from 33 Zettabytes (ZB) in 2018 to 175 ZB in 2025 [1]. That’s equal to a trillion gigabytes.

Big data technologies are software tools used to manage all types of data sets and transform them into business insights. In data science careers, such as big data engineers, sophisticated analytics evaluate and process huge volumes of data. 

Here are the four types of big data technologies and the tools that can be used to learn them.

4 types of big data technologies

Big data technologies can be categorised into four main types: data storage, data mining, data analytics, and data visualisation [2]. Each of these is associated with certain tools, and depending on the type of big data technology required, you’ll want to choose the right tool for your business needs.

1. Data storage

Big data technology that deals with data storage can fetch, store, and manage big data. It is made up of infrastructure that allows users to store the data so that it is convenient to access. Most data storage platforms are compatible with other programs. Two commonly used tools are Apache Hadoop and MongoDB. 

• Apache Hadoop: Apache is the most widely used big data tool. It is an open-source software platform that stores and processes big data in a distributed computing environment across hardware clusters. This distribution allows for faster data processing. The framework is designed to reduce bugs or faults, be scalable, and process all data formats.

• MongoDB: MongoDB is a NoSQL database storing large volumes of data. Using key-value pairs (a basic unit of data), MongoDB categorises documents into collections. It is written in C, C++, and JavaScript and is one of the most popular big data databases because it can easily manage and store unstructured data.

2. Data mining

Data mining extracts useful patterns and trends from the raw data. Big data technologies like Rapidminer and Presto can turn unstructured and structured data into usable information.

• Rapidminer: Rapidminer is a data mining tool that can be used to build predictive models. It draws on these two roles as strengths of processing and preparing data and building machine and deep learning models. The end-to-end model allows both functions to drive impact across the organisation [3].

• Presto: Presto is an open-source query engine originally developed by Facebook to run analytic queries against its large data sets. Now, it is available widely. One query on Presto can combine data from multiple sources within an organisation and perform analytics on them in minutes.

3. Data analytics

In big data analytics, technologies clean and transform data into information that can drive business decisions. This next step (after data mining) is where users perform algorithms and models using tools such as Apache Spark and Splunk.

• Apache Spark: Spark is a popular big data tool for data analysis because it is fast and efficient at running applications. It is faster than Hadoop because it uses random access memory (RAM) instead of being stored and processed in batches via MapReduce [4]. Spark supports a wide variety of data analytics tasks and queries.

• Splunk: Splunk is another popular big data analytics tool for deriving insights from large datasets. It can generate graphs, charts, reports, and dashboards. Splunk also enables users to incorporate artificial intelligence (AI) into data outcomes.

4. Data visualisation

Finally, big data technologies can create stunning visualisations from the data. In data-oriented roles, data visualisation is a skill that is beneficial for presenting recommendations to stakeholders for business profitability and operations—to tell an impactful story with a simple graph.

• Tableau: Tableau is a very popular tool for data visualisation. Its drag-and-drop interface makes creating pie charts, bar charts, box plots, Gantt charts, and more easy. It is also a secure platform allowing users to share real-time visualisations and dashboards.

• Looker: Looker is a business intelligence (BI) tool used to make sense of big data analytics and share those insights with other teams. A query can configure charts, graphs, and dashboards, such as monitoring weekly brand engagement through social media analytics. 

Learn big data with Coursera.

Immerse yourself in the world of big data technologies. Learn all you need to know about big data analysis based on the world’s most popular big data technologies, Hadoop, Spark, and Storm, from Yonsei University’s course Big Data Emerging Technologies on Coursera, part of the specialisation Emerging Technologies: From Smartphones to IoT to Big Data.

If you want to focus on big data more broadly, the University of California San Diego’s Big Data Specialisation might be your choice. You’ll learn the basics of Hadoop and Spark with guidance from the professor. Get started for free with Coursera Plus today.